The Cryptanalysis of a Three Rotor Machine Using a Genetic Algorithm. - Anthony J. Bagnall; Geoff P. McKeown; Victor J. Rayward-Smith (ICGA, 1997)

Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity. - Anthony J. Bagnall; George D. Smith (GECCO, 1999)

A Multi-Adaptive Agent Model of Generator Bidding in the UK Market in Electricity. - Anthony J. Bagnall (GECCO, 2000)

The next release problem. - Anthony J. Bagnall; Victor J. Rayward-Smith; Ian M. Whittley (Inf. Softw. Technol., 2001)

An Agent Model for First Price and Second Price Private Value Auctions. - Anthony J. Bagnall; Iain Toft (Artificial Evolution, 2003)

Data mining rules using multi-objective evolutionary algorithms. - Beatriz de la Iglesia; M. S. Philpott; Anthony J. Bagnall; Victor J. Rayward-Smith (IEEE Congress on Evolutionary Computation, 2003)

Learning classifier systems for data mining: a comparison of XCS with other classifiers for the Forest Cover data set. - Anthony J. Bagnall; Gavin C. Cawley (IJCNN, 2003)

Improving Decision Tree Performance Through Induction- and Cluster-Based Stratified Sampling. - Abdul Aziz Gill; George D. Smith; Anthony J. Bagnall (IDEAL, 2004)

Clustering time series from ARMA models with clipped data. - Anthony J. Bagnall; Gareth J. Janacek (KDD, 2004)

On the use of rule-sharing in learning classifier system ensembles. - Larry Bull; Matthew Studley; Anthony J. Bagnall; Ian M. Whittley (Congress on Evolutionary Computation, 2005)

AgentP classifier system: self-adjusting vs. gradual approach. - Zhanna V. Zatuchna; Anthony J. Bagnall (Congress on Evolutionary Computation, 2005)

A Likelihood Ratio Distance Measure for the Similarity Between the Fourier Transform of Time Series. - Gareth J. Janacek; Anthony J. Bagnall; M. Powell (PAKDD, 2005)

A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. - Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh; Anthony J. Bagnall; Stefano Lonardi (PAKDD, 2005)

Clustering Time Series with Clipped Data. - Anthony J. Bagnall; Gareth J. Janacek (Mach. Learn., 2005)

A multiagent model of the UK market in electricity generation. - Anthony J. Bagnall; George D. Smith (IEEE Trans. Evol. Comput., 2005)

A Comparison of DWT/PAA and DFT for Time Series Classification. - Anthony J. Bagnall; Ian M. Whittley; Gareth J. Janacek; Kate Kemsley; Matthew Studley; Larry Bull (DMIN, 2006)

Variance Stabilizing Regression Ensembles for Environmental Models. - Anthony J. Bagnall; Ian M. Whittley; Matthew Studley; Mike Pettipher; Firat Tekiner; Larry Bull (IJCNN, 2006)

Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions. - Anthony J. Bagnall; Iain Toft (Auton. Agents Multi Agent Syst., 2006)

A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. - Anthony J. Bagnall; Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh; Stefano Lonardi; Gareth J. Janacek (Data Min. Knowl. Discov., 2006)

Adaptive Sniping for Volatile and Stable Continuous Double Auction Markets. - Iain Toft; Anthony J. Bagnall (AMEC/TADA, 2007)

Super Computer Heterogeneous Classifier Meta-Ensembles. - Anthony J. Bagnall; Gavin C. Cawley; Ian M. Whittley; Larry Bull; Matthew Studley; Mike Pettipher; Firat Tekiner (Int. J. Data Warehous. Min., 2007)

Learning Classifier System Ensembles With Rule-Sharing. - Larry Bull; Matthew Studley; Anthony J. Bagnall; Ian M. Whittley (IEEE Trans. Evol. Comput., 2007)

Time Series Data Mining Algorithms for Identifying Short RNA in Arabidopsis thaliana. - Anthony J. Bagnall; Simon Moxon; David J. Studholme; Vincent Moulton (BIOCOMP, 2008)

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception. - Zhanna V. Zatuchna; Anthony J. Bagnall (Adapt. Behav., 2009)

A learning classifier system for mazes with aliasing clones. - Zhanna V. Zatuchna; Anthony J. Bagnall (Nat. Comput., 2009)

A Randomized Sphere Cover Classifier. - Reda Younsi; Anthony J. Bagnall (IDEAL, 2010)

On the Extraction and Classification of Hand Outlines. - Luke M. Davis; Barry-John Theobald; Andoni Toms; Anthony J. Bagnall (IDEAL, 2011)

Classification of Household Devices by Electricity Usage Profiles. - Jason Lines; Anthony J. Bagnall; Patrick Caiger-Smith; Simon Anderson (IDEAL, 2011)

Automated Bone Age Assessment Using Feature Extraction. - Luke M. Davis; Barry-John Theobald; Anthony J. Bagnall (IDEAL, 2012)

Interestingness Measures for Fixed Consequent Rules. - Jon Hills; Luke M. Davis; Anthony J. Bagnall (IDEAL, 2012)

Alternative Quality Measures for Time Series Shapelets. - Jason Lines; Anthony J. Bagnall (IDEAL, 2012)

A shapelet transform for time series classification. - Jason Lines; Luke M. Davis; Jon Hills; Anthony J. Bagnall (KDD, 2012)

Transformation Based Ensembles for Time Series Classification. - Anthony J. Bagnall; Luke M. Davis; Jon Hills; Jason Lines (SDM, 2012)

An efficient randomised sphere cover classifier. - Reda Younsi; Anthony J. Bagnall (Int. J. Data Min. Model. Manag., 2012)

On the Segmentation and Classification of Hand Radiographs. - Luke M. Davis; Barry-John Theobald; Jason Lines; Andoni Toms; Anthony J. Bagnall (Int. J. Neural Syst., 2012)

Clupea Harengus: Intraspecies Distinction using Curvature Scale Space and Shapelets - Classification of North-sea and Thames Herring using Boundary Contour of Sagittal Otoliths. - James Mapp; Mark Fisher; Anthony J. Bagnall; Jason Lines; Sally Warne; Joe Scutt Phillips (ICPRAM, 2013)

BruteSuppression: a size reduction method for Apriori rule sets. - Jon Hills; Anthony J. Bagnall; Beatriz de la Iglesia; Graeme Richards (J. Intell. Inf. Syst., 2013)

Ensembles of Elastic Distance Measures for Time Series Classification. - Jason Lines; Anthony J. Bagnall (SDM, 2014)

A Run Length Transformation for Discriminating Between Auto Regressive Time Series. - Anthony J. Bagnall; Gareth J. Janacek (J. Classif., 2014)

Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes. - Anthony J. Bagnall; Luke M. Davis (CoRR, 2014)

Finding Motif Sets in Time Series. - Anthony J. Bagnall; Jon Hills; Jason Lines (CoRR, 2014)

An Experimental Evaluation of Nearest Neighbour Time Series Classification. - Anthony J. Bagnall; Jason Lines (CoRR, 2014)

Classification of time series by shapelet transformation. - Jon Hills; Jason Lines; Edgaras Baranauskas; James Mapp; Anthony J. Bagnall (Data Min. Knowl. Discov., 2014)

Benchmarking the semi-supervised naïve Bayes classifier. - Awat A. Saeed; Gavin C. Cawley; Anthony J. Bagnall (IJCNN, 2015)

Time series classification with ensembles of elastic distance measures. - Jason Lines; Anthony J. Bagnall (Data Min. Knowl. Discov., 2015)

Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles. - Anthony J. Bagnall; Jason Lines; Jon Hills; Aaron Bostrom (IEEE Trans. Knowl. Data Eng., 2015)

HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification. - Jason Lines; Sarah Taylor; Anthony J. Bagnall (ICDM, 2016)

The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version. - Anthony J. Bagnall; Aaron Bostrom; James Large; Jason Lines (CoRR, 2016)

Ensembles of random sphere cover classifiers. - Reda Younsi; Anthony J. Bagnall (Pattern Recognit., 2016)

Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series. - Hoang Anh Dau; Diego Furtado Silva; François Petitjean; Germain Forestier; Anthony J. Bagnall; Eamonn J. Keogh (IEEE BigData, 2017)

Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings. - Anthony J. Bagnall; Aaron Bostrom; James Large; Jason Lines (CoRR, 2017)

On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms. - Anthony J. Bagnall; Gavin C. Cawley (CoRR, 2017)

The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts. - James Large; Jason Lines; Anthony J. Bagnall (CoRR, 2017)

A Shapelet Transform for Multivariate Time Series Classification. - Aaron Bostrom; Anthony J. Bagnall (CoRR, 2017)

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. - Anthony J. Bagnall; Jason Lines; Aaron Bostrom; James Large; Eamonn J. Keogh (Data Min. Knowl. Discov., 2017)

Binary Shapelet Transform for Multiclass Time Series Classification. - Aaron Bostrom; Anthony J. Bagnall (Trans. Large Scale Data Knowl. Centered Syst., 2017)

Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios. - Shaghayegh Gharghabi; Shima Imani; Anthony J. Bagnall; Amirali Darvishzadeh; Eamonn J. Keogh (ICDM, 2018)

Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning. - James Large; E. Kate Kemsley; Nikolaus Wellner; Ian Goodall; Anthony J. Bagnall (PAKDD (1), 2018)

Is rotation forest the best classifier for problems with continuous features? - Anthony J. Bagnall; Aaron Bostrom; Gavin C. Cawley; Michael Flynn; James Large; Jason Lines (CoRR, 2018)

From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers. - James Large; Anthony J. Bagnall; Simon Malinowski; Romain Tavenard (CoRR, 2018)

The UEA multivariate time series classification archive, 2018. - Anthony J. Bagnall; Hoang Anh Dau; Jason Lines; Michael Flynn; James Large; Aaron Bostrom; Paul Southam; Eamonn J. Keogh (CoRR, 2018)

Optimizing dynamic time warping's window width for time series data mining applications. - Hoang Anh Dau; Diego Furtado Silva; François Petitjean; Germain Forestier; Anthony J. Bagnall; Abdullah Mueen; Eamonn J. Keogh (Data Min. Knowl. Discov., 2018)

Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles. - Jason Lines; Sarah Taylor; Anthony J. Bagnall (ACM Trans. Knowl. Discov. Data, 2018)

Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? - James Large; Paul Southam; Anthony J. Bagnall (HAIS, 2019)

Classifying Flies Based on Reconstructed Audio Signals. - Michael Flynn; Anthony J. Bagnall (IDEAL (2), 2019)

A Hybrid Approach to Time Series Classification with Shapelets. - David Guijo-Rubio; Pedro Antonio Gutiérrez; Romain Tavenard; Anthony J. Bagnall (IDEAL (1), 2019)

Mixing Hetero- and Homogeneous Models in Weighted Ensembles. - James Large; Anthony J. Bagnall (IDEAL (1), 2019)

Scalable Dictionary Classifiers for Time Series Classification. - Matthew Middlehurst; William Vickers; Anthony J. Bagnall (IDEAL (1), 2019)

A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency. - Anthony J. Bagnall; Franz J. Király; Markus Löning; Matthew Middlehurst; George Oastler (CoRR, 2019)

sktime: A Unified Interface for Machine Learning with Time Series. - Markus Löning; Anthony J. Bagnall; Sajaysurya Ganesh; Viktor Kazakov; Jason Lines; Franz J. Király (CoRR, 2019)

A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers. - Anthony J. Bagnall; James Large; Matthew Middlehurst (CoRR, 2019)

Data Series Management (Dagstuhl Seminar 19282). - Anthony J. Bagnall; Richard L. Cole; Themis Palpanas; Konstantinos Zoumpatianos (Dagstuhl Reports, 2019)

A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. - James Large; Jason Lines; Anthony J. Bagnall (Data Min. Knowl. Discov., 2019)

On time series classification with dictionary-based classifiers. - James Large; Anthony J. Bagnall; Simon Malinowski; Romain Tavenard (Intell. Data Anal., 2019)

The UCR time series archive. - Hoang Anh Dau; Anthony J. Bagnall; Kaveh Kamgar; Chin-Chia Michael Yeh; Yan Zhu 014; Shaghayegh Gharghabi; Chotirat Ann Ratanamahatana; Eamonn J. Keogh (IEEE CAA J. Autom. Sinica, 2019)

The Canonical Interval Forest (CIF) Classifier for Time Series Classification. - Matthew Middlehurst; James Large; Anthony J. Bagnall (IEEE BigData, 2020)

Time series ordinal classification via shapelets. - David Guijo-Rubio; Pedro Antonio Gutiérrez; Anthony J. Bagnall; César Hervás-Martínez (IJCNN, 2020)

On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0). - Anthony J. Bagnall; Michael Flynn; James Large; Jason Lines; Matthew Middlehurst (AALTD@PKDD/ECML, 2020)

Ordinal Versus Nominal Time Series Classification. - David Guijo-Rubio; Pedro Antonio Gutiérrez; Anthony J. Bagnall; César Hervás-Martínez (AALTD@PKDD/ECML, 2020)

The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification. - Matthew Middlehurst; James Large; Gavin C. Cawley; Anthony J. Bagnall (ECML/PKDD (1), 2020)

Developing Ensemble Methods for Detecting Anomalies in Water Level Data. - Thakolpat Khampuengson; Anthony J. Bagnall; Wenjia Wang (SGAI Conf., 2020)

A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0. - Anthony J. Bagnall; Michael Flynn; James Large; Jason Lines; Matthew Middlehurst (CoRR, 2020)

Detecting Electric Devices in 3D Images of Bags. - Anthony J. Bagnall; Paul Southam; James Large; Richard W. Harvey (CoRR, 2020)

Benchmarking Multivariate Time Series Classification Algorithms. - Alejandro Pasos Ruiz; Michael Flynn; Anthony J. Bagnall (CoRR, 2020)

An ultra-fast time series distance measure to allow data mining in more complex real-world deployments. - Shaghayegh Gharghabi; Shima Imani; Anthony J. Bagnall; Amirali Darvishzadeh; Eamonn J. Keogh (Data Min. Knowl. Discov., 2020)

The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. - Alejandro Pasos Ruiz; Michael Flynn; James Large; Matthew Middlehurst; Anthony J. Bagnall (Data Min. Knowl. Discov., 2021)

HIVE-COTE 2.0: a new meta ensemble for time series classification. - Matthew Middlehurst; James Large; Michael Flynn; Jason Lines; Aaron Bostrom; Anthony J. Bagnall (Mach. Learn., 2021)

Dimension Selection Strategies for Multivariate Time Series Classification with HIVE-COTEv2.0. - Alejandro Pasos Ruiz; Anthony J. Bagnall (AALTD@ECML/PKDD, 2022)

The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier. - Matthew Middlehurst; Anthony J. Bagnall (ICPRAI (2), 2022)

Human Activity Segmentation Challenge @ ECML/PKDD'23. - Arik Ermshaus; Patrick Schäfer 001; Anthony J. Bagnall; Thomas Guyet; Georgiana Ifrim; Vincent Lemaire 001; Ulf Leser; Colin Leverger; Simon Malinowski (AALTD@ECML/PKDD, 2023)

Clustering Time Series with k-Medoids Based Algorithms. - Christopher Holder; David Guijo-Rubio; Anthony J. Bagnall (AALTD@ECML/PKDD, 2023)

Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression. - Matthew Middlehurst; Anthony J. Bagnall (AALTD@ECML/PKDD, 2023)

Barycentre Averaging for the Move-Split-Merge Time Series Distance Measure. - Christopher Holder; David Guijo-Rubio; Anthony J. Bagnall (KDIR, 2023)

Time Series Classification of Electroencephalography Data. - Aiden Rushbrooke; Jordan Tsigarides; Saber Sami; Anthony J. Bagnall (IWANN (1), 2023)

A Hands-on Introduction to Time Series Classification and Regression. - Anthony J. Bagnall; Matthew Middlehurst; Germain Forestier; Ali Ismail-Fawaz; Antoine Guillaume; David Guijo-Rubio; Chang Wei Tan; Angus Dempster; Geoffrey I. Webb (KDD, 2024)

aeon: a Python toolkit for learning from time series. - Matthew Middlehurst; Ali Ismail-Fawaz; Antoine Guillaume; Christopher Holder; David Guijo-Rubio; Guzal Bulatova; Leonidas Tsaprounis; Lukasz Mentel; Martin Walter; Patrick Schäfer 001; Anthony J. Bagnall (CoRR, 2024)

On time series clustering with k-means. - Christopher Holder; Anthony J. Bagnall; Jason Lines (CoRR, 2024)

Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering. - Christopher Holder; Anthony J. Bagnall (CoRR, 2024)

A review and evaluation of elastic distance functions for time series clustering. - Christopher Holder; Matthew Middlehurst; Anthony J. Bagnall (Knowl. Inf. Syst., 2024)

Unsupervised feature based algorithms for time series extrinsic regression. - David Guijo-Rubio; Matthew Middlehurst; Guilherme Arcencio; Diego Furtado Silva; Anthony J. Bagnall (Data Min. Knowl. Discov., 2024)

Bake off redux: a review and experimental evaluation of recent time series classification algorithms. - Matthew Middlehurst; Patrick Schäfer 001; Anthony J. Bagnall (Data Min. Knowl. Discov., 2024)

Correction: Bake off redux: a review and experimental evaluation of recent time series classification algorithms. - Matthew Middlehurst; Patrick Schäfer 001; Anthony J. Bagnall (Data Min. Knowl. Discov., 2024)

Convolutional- and Deep Learning-Based Techniques for Time Series Ordinal Classification. - Rafael Ayllón-Gavilán; David Guijo-Rubio; Pedro Antonio Gutiérrez; Anthony J. Bagnall; César Hervás-Martínez (IEEE Trans. Cybern., 2025)